Matteo Sali's research while affiliated with Italian National Research Council and other places
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Publications (8)
The availability of high-resolution reference datasets representing in space and time and with high accuracy areas affected by fires is strategic for the validation of remotely-sensed Burned Area (BA) products. This paper proposes a methodology designed to build a burned area reference dataset from Sentinel-2 (S2) images at continental scale by imp...
Coarse resolution sensors are not very sensitive at detecting small fire patches, making current estimations of global burned areas (BA) very conservative. Using medium or high-resolution sensors to generate BA products becomes then a priority, particularly in areas where fires tend to be small and frequent.
Building on previous work that developed...
The paper proposes a fully automatic algorithm approach to map burned areas from remote sensing characterized by human interpretable mapping criteria and explainable results. This approach is partially knowledge-driven and partially data-driven. It exploits active fire points to train the fusion function of factors deemed influential in determining...
Sentinel-2 (S2) multi-spectral instrument (MSI) images are used in an automated approach built on fuzzy set theory and a region growing (RG) algorithm to identify areas affected by fires in Mediterranean regions. S2 spectral bands and their post- and pre-fire date (Dpost-pre) difference are interpreted as evidence of burn through soft constraints o...
Citations
... In this case, freely available images with higher resolution are difficult to find. Planet data were used as reference in [3], [16]. In [11], Landsat data were exploited to validate S2-derived BAs, while in [4] two different teams generated S2-derived BA maps. ...
... However, there is also an increasing interest in the use of moderate resolution data for the generation of BA products. Previous investigations demonstrated that both Landsat and Sentinel-2 (S2) data are able to map small and/or fragmented BAs [3], while coarse resolution data have difficulties in detecting small fires (< 100 ha) [4]. ...
... The sooner the image is acquired after the fire event the easier is the detection due to a stronger spectral signature of burn, as a consequence of fire on vegetation compound. For this reason, in general, we select the first available clear image after the event [36]. In this study, the temporal difference between pre-and post-fire images is on average 20 days. ...
... "Burn severity" is a term used to describe the physical, chemical, and biological alterations for the purpose of identifying the burned area after a fire (White et al., 1996). Furthermore, several techniques are used to create fire risk maps, including weighted registration, machine learning, deep learning, and frequency ratio (Javad et al., 2014;Tien Bui et al., 2017;Stroppiana et al., 2021). On the other hand, the land surface temperature (LST) is related to burned areas based on the severity of the forest fire (Vlassova et. ...
... Fire activity has significant implications for ecological communities, biogeochemical cycles, climate, and human lives and assets. Approximately over half of the Earth's land surface is susceptible to fire, with around 3% experiencing annual burning according to coarse-resolution satellites [1], a value that is probably much higher according to recent estimates from finer satellite imagery [2]. Because of the vast extent of land burned over the world, landscape fires release approximately 23% of the global CO 2 emitted annually from fossil fuels, modify Earth's energy fluxes through changes in surface albedo, and have an enormous influence on human health and the economy [1]. ...